Dylan Montenegro
5/3/2022
Salaries <- Salaries %>% rename(Base_Salary = `Yearly brutto salary (without bonus and stocks) in EUR`)
Salaries <- Salaries %>% select(- c(Timestamp, `Years of experience in Germany`, `Yearly bonus + stocks in EUR`, `Other technologies/programming languages you use often`, `Annual brutto salary (without bonus and stocks) one year ago. Only answer if staying in the same country`, `Annual bonus+stocks one year ago. Only answer if staying in same country`, `Have you lost your job due to the coronavirus outbreak?`, `Have you been forced to have a shorter working week (Kurzarbeit)? If yes, how many hours per week`, `Have you received additional monetary support from your employer due to Work From Home? If yes, how much in 2020 in EUR`, `Employment status`, `Number of vacation days`))
Salaries <- Salaries %>% group_by(City) %>% mutate(number_responses_per_city = n())
Salaries <- Salaries %>% group_by(Position) %>% mutate(number_people_per_position = n())
Salaries <- Salaries %>% group_by(`Your main technology / programming language`) %>% mutate(number_people_per_main_tech= n())
x <- Salaries$`Your main technology / programming language`
Salaries$`Your main technology / programming language` <- toupper(x)
main_tech_swap <- function(old, new1){
test <- Salaries
test$`Your main technology / programming language`[test$`Your main technology / programming language` == old] <- new1
return(test)
}
experience_swap <- function(old2, new2){
test2 <- Salaries
test2$`Total years of experience`[test2$`Total years of experience` == old2] <- new2
return(test2)
}
company_type_swap <- function(old4, new4){
test4 <- Salaries
test4$`Company type`[test4$`Company type` == old4] <- new4
return(test4)
}
city_swap <- function(old5, new5){
test5 <- Salaries
test5$`City`[test5$`City` == old5] <- new5
return(test5)
}
main_language_swap <- function(old6, new6){
test6 <- Salaries
test6$`Main language at work`[test6$`Main language at work` == old6] <- new6
return(test6)
}
seniority_level_swap <- function(old7, new7){
test7 <- Salaries
test7$`Seniority level`[test7$`Seniority level` == old7] <- new7
return(test7)
}
position_swap <- function(old8, new8){
test8 <- Salaries
test8$`Position`[test8$`Position` == old8] <- new8
return(test8)
}## [1] "Age"
## [2] "Gender"
## [3] "City"
## [4] "Position"
## [5] "Total years of experience"
## [6] "Seniority level"
## [7] "Your main technology / programming language"
## [8] "Base_Salary"
## [9] "Сontract duration"
## [10] "Main language at work"
## [11] "Company size"
## [12] "Company type"
## [13] "number_responses_per_city"
## [14] "number_people_per_position"
## [15] "number_people_per_main_tech"
## # A tibble: 3 × 2
## Gender n
## <chr> <int>
## 1 Male 1049
## 2 Female 192
## 3 Diverse 2
## # A tibble: 2 × 2
## Gender n
## <chr> <int>
## 1 Male 643
## 2 Female 115
## # A tibble: 10 × 2
## City n
## <chr> <int>
## 1 Berlin 681
## 2 Munich 236
## 3 Frankfurt 44
## 4 Hamburg 40
## 5 Stuttgart 33
## 6 Cologne 20
## 7 Düsseldorf 15
## 8 Amsterdam 9
## 9 Karlsruhe 7
## 10 Nürnberg 7
## # A tibble: 7 × 2
## City n
## <chr> <int>
## 1 Berlin 495
## 2 Munich 159
## 3 Frankfurt 30
## 4 Hamburg 30
## 5 Stuttgart 24
## 6 Cologne 13
## 7 Düsseldorf 7
## # A tibble: 10 × 2
## Position n
## <chr> <int>
## 1 Software Engineer 388
## 2 Backend Developer 174
## 3 Data Scientist 110
## 4 Frontend Developer 89
## 5 QA Engineer 71
## 6 DevOps 57
## 7 Mobile Developer 53
## 8 ML Engineer 42
## 9 Product Manager 39
## 10 Data Engineer 28
## # A tibble: 10 × 2
## Position n
## <chr> <int>
## 1 Software Engineer 243
## 2 Backend Developer 124
## 3 Data Scientist 65
## 4 QA Engineer 49
## 5 Frontend Developer 41
## 6 Product Manager 31
## 7 Mobile Developer 28
## 8 DevOps 24
## 9 ML Engineer 24
## 10 Data Engineer 22
## # A tibble: 6 × 2
## `Seniority level` n
## <chr> <int>
## 1 Senior 565
## 2 Middle 366
## 3 Lead 166
## 4 Junior 79
## 5 Head 44
## 6 Entry level 4
## # A tibble: 6 × 2
## `Seniority level` n
## <chr> <int>
## 1 Senior 355
## 2 Middle 211
## 3 Lead 104
## 4 Junior 38
## 5 Head 36
## 6 Entry level 2
## # A tibble: 5 × 2
## `Main language at work` n
## <chr> <int>
## 1 English 1024
## 2 German 186
## 3 Russian 15
## 4 Italian 3
## 5 Spanish 3
## # A tibble: 4 × 2
## `Main language at work` n
## <chr> <int>
## 1 English 649
## 2 German 97
## 3 Russian 3
## 4 Deuglisch 1
## # A tibble: 10 × 2
## `Your main technology / programming language` n
## <chr> <int>
## 1 PYTHON 228
## 2 JAVA 216
## 3 JAVASCRIPT 116
## 4 C 98
## 5 PHP 73
## 6 GO 32
## 7 TYPESCRIPT 31
## 8 SWIFT 30
## 9 SCALA 28
## 10 KOTLIN 27
## # A tibble: 10 × 2
## `Your main technology / programming language` n
## <chr> <int>
## 1 JAVA 169
## 2 PYTHON 159
## 3 JAVASCRIPT 74
## 4 C 51
## 5 PHP 50
## 6 SCALA 23
## 7 GO 22
## 8 KOTLIN 20
## 9 RUBY 19
## 10 SWIFT 19
## # A tibble: 5 × 2
## `Company size` n
## <chr> <int>
## 1 101-1000 273
## 2 1000+ 262
## 3 11-50 100
## 4 51-100 86
## 5 up to 10 26
## # A tibble: 5 × 2
## `Company type` n
## <chr> <int>
## 1 Product 481
## 2 Startup 161
## 3 Consulting / Agency 71
## 4 Ecommerce 7
## 5 Outsource 3